The mass spectrometry principle consists in ionizing chemical elements to generate charged molecules or molecule fragments for which the mass-to-charge ratio will be measured. Mass spectra of ions, for example in a solution, provide a distribution of the ions by mass-to-charge ratio. The x-axis of a mass spectrum graph gives the mass-to-charge ratio identifying one ion and the y-axis gives the signal intensity provided by these ions. A mass spectrum graph for one ion gives the (mass-to-charge, intensity) information for the ion read at the peak. For a chemical solution containing different ions, each peak of the mass spectrum graph may indicate the presence of a corresponding ion in the solution.
However it is not always easy to identify ions in a mass spectrum of a chemical solution when the mass spectrum graph contains a dense sequence of peaks and when—for each peak—both the mass and the intensity measured by the instrument are subject to errors. For instance, we may need to predict concentration of a soluble substance in a solution by building regression models, the concentration of some ions in the solution being an unknown yet definite function of the substance concentration. In the linear case the ions concentrations are related to the substance concentration by coefficients which are different for different ions. In order to build the regression models we must first be able to identify the peak intensity corresponding to a same ion in different mass spectra for different concentrations of a substance in a solution. When the substance corresponds to inorganic molecules the ions are easily identified in the mass spectra. However, in the case where an organic molecule is diluted in water the mass spectrum of the resulting solution may include hundreds of ions, due to the dissociation of the large molecule in water.
One prior art solution to identify in different mass spectra corresponding to different concentration of a substance in a solution, the information corresponding to a same ion, is to use a well known data binning technique. The data binning technique allows to reduce the effect of minor measurement errors: in the mass spectrum the mass range should be covered by non-overlapping intervals (bins) of uniform size (usually of one mass unit) and the intensity of each peak is accumulated into the corresponding bin. However, let me be the error associated to the ion mass measurement across all solutions to be analyzed for building the regression model, two effects related to the error me may undermine the binning approach, namely:
With a bin size comparable to (or smaller than) me it is likely that the peak of a given ion in different spectra would be accumulated in different bins;
With a bin size larger than me it generally happens that the peaks of two or more ions with similar masses are accumulated in the same bin. However those ions may have a totally different linear dependency on the substance concentration and, because of the previous effect, the same bin may accumulate the contributions from different ions across different spectra.
There is thus a need to have a method for identification of in mass spectra graph peaks corresponding to the same physical ion across solutions prepared with different known concentrations of a substance.